Search results for: thermal cycling machine

Authors: Bayarjargal Enkhtaivan, Gao Wei, Zhang Yanping, He Guo Qiang

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In our study, traditional cleaning mirror technology with reduced consumption of water in solar thermal power plants is investigated. In developed countries, a significant increase of growth and innovation in solar thermal power sector is evident since over the last decade. These power plants required higher water consumption, however, there are some complications to construct and operate such power plants under severe drought-inflicted areas like deserts where high water-deficit can be seen but sufficient solar energy is available. Designing new experimental equipments is the most important advantage of this study. These equipments can estimate various types of measurements at the mean time. In this study, Glasses were placed for 10 and 20 days at certain positions to deposit dusts on glass surface by using a common method. Dust deposited on glass surface was washed by experimental equipment and measured dust deposition on each glass. After that, experimental results were analyzed and concluded.

Keywords: concentrated solar power (CSP) plant, high-pressure water, test equipment of clean mirror, cleaning technology of glass and mirror

Procedia PDF Downloads 168

Authors: O. Sunantha, S. Zhenfeng, S. Phattraporn, A. Zeeshan

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The decline of soil organic carbon (SOC) in global agriculture is a critical issue requiring rapid and accurate estimation for informed policymaking. While it is recognized that SOC predictors vary significantly when derived from remote sensing data and environmental variables, identifying the specific parameters most suitable for accurately estimating SOC in diverse agricultural areas remains a challenge. This study utilizes remote sensing data to precisely estimate SOC and identify influential factors in diverse agricultural areas, such as paddy, corn, sugarcane, cassava, and perennial crops. Extreme gradient boosting (XGBoost), random forest (RF), and support vector regression (SVR) models are employed to analyze these factors' impact on SOC estimation. The results show key factors influencing SOC estimation include slope, vegetation indices (EVI), spectral reflectance indices (red index, red edge2), temperature, land use, and surface soil moisture, as indicated by their averaged importance scores across XGBoost, RF, and SVR models. Therefore, using different machine learning algorithms for SOC estimation reveals varying influential factors from remote sensing data and environmental variables. This approach emphasizes feature selection, as different machine learning algorithms identify various key factors from remote sensing data and environmental variables for accurate SOC estimation.

Keywords: factors influencing SOC estimation, remote sensing data, environmental variables, machine learning

Procedia PDF Downloads 20

Authors: N. Demirsoy, N. Uçar, A. Önen, N. Kızıldağ, Ö. F. Vurur, O. Eren, İ. Karacan

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Polymer composite nano-fibers including (1, 3 wt %) silver nano-particles have been produced by electrospinning method. Polyacrylonitrile/N,N-dimethylformamide (PAN/DMF) solution has been prepared and the amount of silver nitrate has been adjusted to PAN weight. Silver nano-particles were obtained from reduction of silver ions into silver nano-particles by chemical reduction by hydrazine hydroxide (N2H5OH). The different amount of silver salt was loaded into polymer matrix to obtain polyacrylonitrile composite nano-fiber containing silver nano-particles. The effect of the amount of silver nano-particles on the properties of composite nano-fiber web was investigated. Electrical conductivity, mechanical properties, thermal properties were examined by Microtest LCR Meter 6370 (0.01 mΩ-100 MΩ), tensile tester, differential scanning calorimeter DSC (Q10) and SEM, respectively. Also, antimicrobial efficiency test (ASTM E2149-10) was done against Staphylococcus aureus bacteria. It has been seen that breaking strength, conductivity, antimicrobial effect, enthalpy during cyclization increase by use of silver nano-particles while the diameter of nano-fiber decreases.

Keywords: composite polyacrylonitrile nanofiber, electrical conductivity, electrospinning, mechanical properties, thermal properties, silver nanoparticles

Procedia PDF Downloads 413

Authors: Ismail Rahama Adam Hamid

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This paper presents the development of a thermal model for a flat, double-sided linear generator designed for use in free-piston engines. The study conducted in this paper examines the influence of temperature on the performance of the permeant magnet linear generator, an integral and pivotal component within the system. This research places particular emphasis on the Neodymium Iron Boron (NdFeB) permanent magnet, which serves as a source of magnetic field for the linear generator. In this study, an internal combustion engine that tends to produce heat is connected to a generator. Considering the temperatures rise from both the combustion process and the thermal contributions of current-carrying conductors and frictional forces. Utilizing Computational Fluid Dynamics (CFD) method, a thermal model of the (NdFeB) magnet within the linear generator is constructed and analyzed. Furthermore, the temperature field is examined to ensure that the linear generator operates under stable conditions without the risk of demagnetization.

Keywords: free piston engine, permanent magnet, linear generator, demagnetization, simulation

Procedia PDF Downloads 45

Authors: Jean Marie Tshimula, Atsushi Togashi

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We develop a data-science approach for providing an interactive visualization and predictive models to find insights into the projects' historical data in order for stakeholders understand some unseen opportunities in the African market that might escape them behind the online project portfolio of the African Development Bank. This machine learning-based web application identifies the market trend of the fastest growing economies across the continent as well skyrocketing sectors which have a significant impact on the future of business in Africa. Owing to this, the approach is tailored to predict where the investment needs are the most required. Moreover, we create a corpus that includes the descriptions of over more than 1,200 projects that approximately cover 14 sectors designed for some of 53 African countries. Then, we sift out this large amount of semi-structured data for extracting tiny details susceptible to contain some directions to follow. In the light of the foregoing, we have applied the combination of Latent Dirichlet Allocation and Random Forests at the level of the analysis module of our methodology to highlight the most relevant topics that investors may focus on for investing in Africa.

Keywords: machine learning, topic modeling, natural language processing, big data

Procedia PDF Downloads 164

Authors: Xu Jiaqiao

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Text sentiment analysis is an important branch of natural language processing. This technology is widely used in public opinion analysis and web surfing recommendations. At present, the mainstream sentiment analysis methods include three parts: sentiment analysis based on a sentiment dictionary, based on traditional machine learning, and based on deep learning. This paper mainly analyzes and compares the advantages and disadvantages of the SVM method of traditional machine learning and the Long Short-term Memory (LSTM) method of deep learning in the field of Chinese sentiment analysis, using Chinese comments on Sina Microblog as the data set. Firstly, this paper classifies and adds labels to the original comment dataset obtained by the web crawler, and then uses Jieba word segmentation to classify the original dataset and remove stop words. After that, this paper extracts text feature vectors and builds document word vectors to facilitate the training of the model. Finally, SVM and LSTM models are trained respectively. After accuracy calculation, it can be obtained that the accuracy of the LSTM model is 85.80%, while the accuracy of SVM is 91.07%. But at the same time, LSTM operation only needs 2.57 seconds, SVM model needs 6.06 seconds. Therefore, this paper concludes that: compared with the SVM model, the LSTM model is worse in accuracy but faster in processing speed.

Keywords: sentiment analysis, support vector machine, long short-term memory, Chinese microblog comments

Procedia PDF Downloads 84

Authors: B. Ramesh Naik, T. Venugopal

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This paper presents a novel approach which improves the high-level semantics of images based on machine learning approach. The contemporary approaches for image retrieval and object recognition includes Fourier transforms, Wavelets, SIFT and HoG. Though these descriptors helpful in a wide range of applications, they exploit zero order statistics, and this lacks high descriptiveness of image features. These descriptors usually take benefit of primitive visual features such as shape, color, texture and spatial locations to describe images. These features do not adequate to describe high-level semantics of the images. This leads to a gap in semantic content caused to unacceptable performance in image retrieval system. A novel method has been proposed referred as discriminative learning which is derived from machine learning approach that efficiently discriminates image features. The analysis and results of proposed approach were validated thoroughly on WANG and Caltech-101 Databases. The results proved that this approach is very competitive in content-based image retrieval.

Keywords: CBIR, discriminative learning, region weight learning, scale invariant feature transforms

Procedia PDF Downloads 173

Authors: M. Cangi, T. Dolar, C. Ersoy, Y. E. Aydogdu, A. I. Aydeniz, A. Mugan

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The drum is one of the critical parts in a washing machine in which the clothes are washed and spin by the rotational movement. It is activated by the drum shaft which is attached to an electric motor and subjected to dynamic loading. Being one of the critical components, failures of the drum require costly repairs of dynamic components. In this study, tolerance bands between the drum shaft and its two bearings were examined to develop a relationship between the fatigue life of the shaft and the interaction tolerances. Optimization of tolerance bands was completed in consideration of the fatigue life of the shaft as the cost function. The following methodology is followed: multibody dynamic model of a washing machine was constructed and used to calculate dynamic loading on the components. Then, these forces were used in finite element analyses to calculate the stress field in critical components which was used for fatigue life predictions. The factors affecting the fatigue life were examined to find optimum tolerance grade for a given test condition. Numerical results were verified by experimental observations.

Keywords: fatigue life, finite element analysis, tolerance analysis, optimization

Procedia PDF Downloads 154

Authors: Simran Kaur, Paul J. Van Geel

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A coupled Thermal-Mechanical-Biological (TMB) model was developed for the analysis of impacts of temperatures on waste stabilization at a Municipal Solid Waste (MSW) landfill in Quebec, Canada using COMSOL Multiphysics, a finite element-based software. For waste placed in landfills in Northern climates during winter months, it can take months or even years before the waste approaches ideal temperatures for biodegradation to occur. Therefore, the proposed model links biodegradation induced strain in MSW to waste temperatures and corresponding heat generation rates as a result of anaerobic degradation. This provides a link between the thermal-biological and mechanical behavior of MSW. The thermal properties of MSW are further linked to density which is tracked and updated in the mechanical component of the model, providing a mechanical-thermal link. The settlement of MSW is modelled based on the concept of viscoelasticity. The specific viscoelastic model used is a single Kelvin – Voight viscoelastic body in which the finite element response is controlled by the elastic material parameters – Young’s Modulus and Poisson’s ratio. The numerical model was validated with 10 years of temperature and settlement data collected from a landfill in Ste. Sophie, Quebec. The coupled TMB modelling framework, which simulates placement of waste lifts as they are placed progressively in the landfill, allows for optimization of several thermal and mechanical parameters throughout the depth of the waste profile and helps in better understanding of temperature dependence of MSW stabilization. The model is able to illustrate how waste placed in the winter months can delay biodegradation-induced settlement and generation of landfill gas. A delay in waste stabilization will impact the utilization of the approved airspace prior to the placement of a final cover and impact post-closure maintenance. The model provides a valuable tool to assess different waste placement strategies in order to increase airspace utilization within landfills operating under different climates, in addition to understanding conditions for increased gas generation for recovery as a green and renewable energy source.

Keywords: coupled model, finite element modeling, landfill, municipal solid waste, waste stabilization

Procedia PDF Downloads 127

Authors: Nishant Rodrigues, Nicole Spanedda, Chilukuri K. Mohan, Arindam Chakraborty

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A crucial objective in quantum chemistry is the computation of the energy levels of chemical systems. This task requires electron-repulsion integrals as inputs, and the steep computational cost of evaluating these integrals poses a major numerical challenge in efficient implementation of quantum chemical software. This work presents a moment-based machine-learning approach for the efficient evaluation of electron-repulsion integrals. These integrals were approximated using linear combinations of a small number of moments. Machine learning algorithms were applied to estimate the coefficients in the linear combination. A random forest approach was used to identify promising features using a recursive feature elimination approach, which performed best for learning the sign of each coefficient but not the magnitude. A neural network with two hidden layers were then used to learn the coefficient magnitudes along with an iterative feature masking approach to perform input vector compression, identifying a small subset of orbitals whose coefficients are sufficient for the quantum state energy computation. Finally, a small ensemble of neural networks (with a median rule for decision fusion) was shown to improve results when compared to a single network.

Keywords: quantum energy calculations, atomic orbitals, electron-repulsion integrals, ensemble machine learning, random forests, neural networks, feature extraction

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Authors: Ayomide Oyedele

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In the dynamic landscape of cybersecurity, "Strategic Cyber Sentinel" emerges as a revolutionary framework, transcending traditional approaches. This paper pioneers a holistic strategy, weaving together threat intelligence, machine learning, and adaptive defenses. Through meticulous real-world simulations, we demonstrate the unprecedented resilience of our framework against evolving cyber threats. "Strategic Cyber Sentinel" redefines proactive threat mitigation, offering a robust defense architecture poised for the challenges of tomorrow.

Keywords: cybersecurity, resilience, threat intelligence, machine learning, adaptive defenses

Procedia PDF Downloads 72

Authors: Avinash Malladhi

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Forensic accounting is a specialized field that involves the application of accounting principles, investigative skills, and legal knowledge to detect and prevent fraud. With the rise of big data and technological advancements, artificial intelligence (AI) and machine learning (ML) algorithms have emerged as powerful tools for forensic accountants to enhance their fraud detection capabilities. In this paper, we review and analyze various AI/ML algorithms that are commonly used in forensic accounting, including supervised and unsupervised learning, deep learning, natural language processing Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Support Vector Machines (SVMs), Decision Trees, and Random Forests. We discuss their underlying principles, strengths, and limitations and provide empirical evidence from existing research studies demonstrating their effectiveness in detecting financial fraud. We also highlight potential ethical considerations and challenges associated with using AI/ML in forensic accounting. Furthermore, we highlight the benefits of these technologies in improving fraud detection and prevention in forensic accounting.

Keywords: AI, machine learning, forensic accounting & fraud detection, anti money laundering, Benford's law, fraud triangle theory

Procedia PDF Downloads 85

Authors: Anvesh Rajak

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This is a new innovation that can be developed. Here I am going to use the waste heat of air conditioner so as to produce the electricity by using the Stirling engine because this waste heat creates the thermal pollution in the environment. The waste heat from the air conditioners has caused a temperature rise of 1°–2°C or more on weekdays in the Tokyo office areas. This heating promotes the heat-island phenomenon in Tokyo on weekdays. Now these air conditioners creates the thermal pollution in the environment and hence rising the temperature of the environment. Air conditioner generally emit the waste heat air whose temperature is about 50°C which heat the environment. Today the demand of energy is increasing tremendously, but available energy lacks in supply. Hence, there is no option for proper and efficient utilization and conservation of energy. In this paper the main stress is given on energy conservation by using technique of utilizing waste heat from Air-conditioning system. Actually the focus is on the use of the waste heat rather than improving the COP of the air- conditioners; if also we improve the COP of air conditioners gradually it would emit some waste heat so I want that waste heat to be used up. As I have used air conditioner’s waste heat to produce electricity so similarly there are various other appliances which emit the waste heat in the surrounding so here also we could use the Stirling engines and Geothermal heat pump concept to produce the electricity and hence can reduce the thermal pollution in the environment.

Keywords: stirling engine, geothermal heat pumps, waste heat, air conditioners

Procedia PDF Downloads 355

Authors: Amreth Chandrasehar

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Machine Learning (ML) Models are developed and run in production to solve various use cases that help organizations to be more efficient and help drive the business. But this comes at a massive development cost and lost business opportunities. According to the Gartner report, 85% of data science projects fail, and one of the factors impacting this is not paying attention to Model Observability. Model Observability helps the developers and operators to pinpoint the model performance issues data drift and help identify root cause of issues. This paper focuses on providing insights into incorporating model observability in model development and operationalizing it in production.

Keywords: model observability, monitoring, drift detection, ML observability platform

Procedia PDF Downloads 103

Authors: Dongkwon Han, Sangho Kim, Sunil Kwon

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Unconventional gas resources have dramatically changed the future energy landscape. Unlike conventional gas resources, the key challenges in unconventional gas have been the requirement that applies to advanced approaches for production forecasting due to uncertainty and complexity of fluid flow. In this study, artificial neural network (ANN) model which integrates machine learning and data-driven approach was developed to predict productivity in shale gas. The database of 129 wells of Eagle Ford shale basin used for testing and training of the ANN model. The Input data related to hydraulic fracturing, well completion and productivity of shale gas were selected and the output data is a cumulative production. The performance of the ANN using all data sets, clustering and variables importance (VI) models were compared in the mean absolute percentage error (MAPE). ANN model using all data sets, clustering, and VI were obtained as 44.22%, 10.08% (cluster 1), 5.26% (cluster 2), 6.35%(cluster 3), and 32.23% (ANN VI), 23.19% (SVM VI), respectively. The results showed that the pre-trained ANN model provides more accurate results than the ANN model using all data sets.

Keywords: unconventional gas, artificial neural network, machine learning, clustering, variables importance

Procedia PDF Downloads 189

Authors: Omid Saligheh

Abstract:

Poly (tri methylene terephthalate) (PTT) nanofibers were prepared by electrospinning, being directly deposited in the form of a random fibers web. The effect of changing processing parameters such as solution concentration and electrospinning voltage on the morphology of the electrospun PTT nanofibers was investigated with scanning electron microscopy (SEM). The electrospun fibers diameter increased with rising concentration and decreased by increasing the electrospinning voltage, thermal and mechanical properties of electrospun fibers were characterized by DSC and tensile testing, respectively.

Keywords: poly tri methylene terephthalate, electrospinning, morphology, thermal behavior, mechanical properties

Procedia PDF Downloads 81

Authors: Jaya Mathew

Abstract:

Many organizations are faced with the challenge of how to analyze and build Machine Learning models using their sensitive telemetry data. In this paper, we discuss how users can leverage the power of R without having to move their big data around as well as a cloud based solution for organizations willing to host their data in the cloud. By using ScaleR technology to benefit from parallelization and remote computing or R Services on premise or in the cloud, users can leverage the power of R at scale without having to move their data around.

Keywords: predictive maintenance, machine learning, big data, cloud based, on premise solution, R

Procedia PDF Downloads 370

Authors: Senesh Wijayarathne

Abstract:

This study focuses on how someone could develop a Buffer Overflow and could use that to exploit the SLMail Server. This study uses a Kali Linux V2018.4 Virtual Machine and Windows 7 - Internet Explorer V8 Virtual Machine (IPv4 Address - 192.168.56.107). This study starts by sending continued bytes to the SLMail Server to find the crashing point range and creating a unique pattern of the length of the crashing point range to control the Extended Instruction Pointer (EIP). Then by sending all characters to SLMail Server, we could observe and find which characters are not rendered properly by the software, also known as Bad Characters. By finding the ‘Jump to the ESP register (JMP ESP) and with the help of ‘Mona Modules’, we could use msfvenom to create a non-stage windows reverse shell payload. By including all the details gathered previously on one script, we could get a system-level reverse shell of the Windows 7 PC. The end of this paper will discuss how to mitigate this vulnerability.

Keywords: slmail server, extended instruction pointer, jump to the esp register, bad characters, virtual machine, windows 7, kali Linux, buffer overflow, Seattle lab, vulnerability

Procedia PDF Downloads 155

Authors: Samra Isadounene, Amar Boukerrou, Dalila Hammiche

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In this work, the composites were prepared with poly(lactic acid) (PLA) and olive husk flour (OHF) with different percentages (10, 20 and 30%) using extrusion method followed by injection molding. The morphological, mechanical properties and thermal behavior of composites were investigated. Tensile strength and elongation at break of composites showed a decreasing trend with increasing fiber content. On the other hand, Young modulus and storage modulus were increased. The addition of OHF resulted in a decrease in thermal stability of composites. The presence of OHF led to an increase in percentage of crystallinity (Xc) of PLA matrix.

Keywords: biopolymers, composites, mechanical properties, poly(lactic acid)

Procedia PDF Downloads 232

Authors: Till Gramberg, Erwin Gross, Christoph Birenbaum

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This paper presents the approach of the Easy2IoT research project. Easy2IoT aims to enable companies in the prefabrication sheet metal and sheet metal processing industry to enter the Industrial Internet of Things (IIoT) with a low-threshold and cost-effective approach. It focuses on the development of physical hardware and software to easily capture machine activities from on a sawing machine, benefiting various stakeholders in the SME value chain, including machine operators, tool manufacturers and service providers. The methodological approach of Easy2IoT includes an in-depth requirements analysis and customer interviews with stakeholders along the value chain. Based on these insights, actions, requirements and potential solutions for smart services are derived. The focus is on providing actionable recommendations, competencies and easy integration through no-/low-code applications to facilitate implementation and connectivity within production networks. At the core of the project is a novel, non-invasive measurement and analysis system that can be easily deployed and made IIoT-ready. This system collects machine data without interfering with the machines themselves. It does this by non-invasively measuring the tension on a sawing machine. The collected data is then connected and analyzed using artificial intelligence (AI) to provide smart services through a platform-based application. Three Smart Services are being developed within Easy2IoT to provide immediate benefits to users: Wear part and product material condition monitoring and predictive maintenance for sawing processes. The non-invasive measurement system enables the monitoring of tool wear, such as saw blades, and the quality of consumables and materials. Service providers and machine operators can use this data to optimize maintenance and reduce downtime and material waste. Optimize Overall Equipment Effectiveness (OEE) by monitoring machine activity. The non-invasive system tracks machining times, setup times and downtime to identify opportunities for OEE improvement and reduce unplanned machine downtime. Estimate CO2 emissions for connected machines. CO2 emissions are calculated for the entire life of the machine and for individual production steps based on captured power consumption data. This information supports energy management and product development decisions. The key to Easy2IoT is its modular and easy-to-use design. The non-invasive measurement system is universally applicable and does not require specialized knowledge to install. The platform application allows easy integration of various smart services and provides a self-service portal for activation and management. Innovative business models will also be developed to promote the sustainable use of the collected machine activity data. The project addresses the digitalization gap between large enterprises and SME. Easy2IoT provides SME with a concrete toolkit for IIoT adoption, facilitating the digital transformation of smaller companies, e.g. through retrofitting of existing machines.

Keywords: smart services, IIoT, IIoT-platform, industrie 4.0, big data

Procedia PDF Downloads 64

Authors: Vimal Jasoliya, Purnank Bhatt, Mit Shah

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The Injection Moulding is one of the principle methods of conversions of plastics into various end products using a very wide range of plastics materials from commodity plastics to specialty engineering plastics. Injection Moulding Machines are rated as per the tonnage force applied. The work present includes Design & Finite Element Analysis of a structure component of injection moulding machine i.e. clamp cylinder. The work of the project is to upgrade the 1300T clamp cylinder to 1500T clamp cylinder for injection moulding machine. The design of existing clamp cylinder of 1300T is checked. Finite Element analysis is carried out for 1300T clamp cylinder in ANSYS Workbench, and the stress values are compared with acceptance criteria and theoretical calculation. The relation between the clamp cylinder diameter and the tonnage capacity has been derived and verified for 1300T clamp cylinder. The same correlation is used to find out the thickness for 1500T clamp cylinder. The detailed design of 1500T cylinder is carried out based on calculated thickness.

Keywords: clamp cylinder, fatigue analysis, finite element analysis, injection moulding machines

Procedia PDF Downloads 330

Authors: E. Cuevas, M. Feigel'man, L. Ioffe, M. Mezard

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The origin of continuous energy spectrum in large disordered interacting quantum systems is one of the key unsolved problems in quantum physics. While small quantum systems with discrete energy levels are noiseless and stay coherent forever in the absence of any coupling to external world, most large-scale quantum systems are able to produce thermal bath, thermal transport and excitation decay. This intrinsic decoherence is manifested by a broadening of energy levels which acquire a finite width. The important question is: What is the driving force and mechanism of transition(s) between two different types of many-body systems - with and without decoherence and thermal transport? Here, we address this question via two complementary approaches applied to the same model of quantum spin-1/2 system with XY-type exchange interaction and random transverse field. Namely, we develop analytical theory for this spin model on a Bethe lattice and implement numerical study of exact level statistics for the same spin model on random graph. This spin model is relevant to the study of pseudogaped superconductivity and S-I transition in some amorphous materials.

Keywords: strongly correlated electrons, quantum phase transitions, superconductor, insulator

Procedia PDF Downloads 575

Authors: Sebastian Kehne, Alexander Epple, Werner Herfs

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A new method for optimal selection of components for multi-axes forward-feed drive systems is proposed in which the choice of motors, gear boxes and ball screw drives is optimized. Essential is here the synchronization of electrical and mechanical frequency behavior of all axes because even advanced controls (like H∞-controls) can only control a small part of the mechanical modes – namely only those of observable and controllable states whose value can be derived from the positions of extern linear length measurement systems and/or rotary encoders on the motor or gear box shafts. Further problems are the unknown processing forces like cutting forces in machine tools during normal operation which make the estimation and control via an observer even more difficult. To start with, the open source Modelica Feed Drive Library which was developed at the Laboratory for Machine Tools, and Production Engineering (WZL) is extended from one axis design to the multi axes design. It is capable to simulate the mechanical, electrical and thermal behavior of permanent magnet synchronous machines with inverters, different gear boxes and ball screw drives in a mechanical system. To keep the calculation time down analytical equations are used for field and torque producing equivalent circuit, heat dissipation and mechanical torque at the shaft. As a first step, a small machine tool with a working area of 635 x 315 x 420 mm is taken apart, and the mechanical transfer behavior is measured with an impulse hammer and acceleration sensors. With the frequency transfer functions, a mechanical finite element model is built up which is reduced with substructure coupling to a mass-damper system which models the most important modes of the axes. The model is modelled with Modelica Feed Drive Library and validated by further relative measurements between machine table and spindle holder with a piezo actor and acceleration sensors. In a next step, the choice of possible components in motor catalogues is limited by derived analytical formulas which are based on well-known metrics to gain effective power and torque of the components. The simulation in Modelica is run with different permanent magnet synchronous motors, gear boxes and ball screw drives from different suppliers. To speed up the optimization different black-box optimization methods (Surrogate-based, gradient-based and evolutionary) are tested on the case. The objective that was chosen is to minimize the integral of the deviations if a step is given on the position controls of the different axes. Small values are good measures for a high dynamic axes. In each iteration (evaluation of one set of components) the control variables are adjusted automatically to have an overshoot less than 1%. It is obtained that the order of the components in optimization problem has a deep impact on the speed of the black-box optimization. An approach to do efficient black-box optimization for multi-axes design is presented in the last part. The authors would like to thank the German Research Foundation DFG for financial support of the project “Optimierung des mechatronischen Entwurfs von mehrachsigen Antriebssystemen (HE 5386/14-1 | 6954/4-1)” (English: Optimization of the Mechatronic Design of Multi-Axes Drive Systems).

Keywords: ball screw drive design, discrete optimization, forward feed drives, gear box design, linear drives, machine tools, motor design, multi-axes design

Procedia PDF Downloads 281

Authors: Ahmet Karakuş, Akif Can Kilic, Emre Alptekin

Abstract:

A growing number of studies have been conducted to determine how well-being may be predicted using well-designed models. It is necessary to investigate the backgrounds of features in order to construct a viable Subjective Well-Being (SWB) model. We have picked the suitable variables from the literature on SWB that are acceptable for real-world data instructions. The goal of this work is to evaluate the model by feeding it with SWB characteristics and then categorizing the stress levels using machine learning methods to see how well it performs on a real dataset. Despite the fact that it is a multiclass classification issue, we have achieved significant metric scores, which may be taken into account for a specific task.

Keywords: machine learning, multiclassification problem, subjective well-being, perceived stress scale

Procedia PDF Downloads 122

Authors: Steffen Illig, Alexander S. Eggeman, Alessandro Troisi, Stephen G. Yeates, John E. Anthony, Henning Sirringhaus

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Large-amplitude intermolecular vibrations in combination with complex shaped transfer integrals generate a thermally fluctuating energetic landscape. The resulting dynamic disorder and its intrinsic presence in organic semiconductors is one of the most fundamental differences to their inorganic counterparts. Dynamic disorder is believed to govern many of the unique electrical and optical properties of organic systems. However, the low energy nature of these vibrations makes it difficult to access them experimentally and because of this we still lack clear molecular design rules to control and reduce dynamic disorder. Applying a novel technique based on electron diffraction we encountered strong intermolecular, thermal vibrations in every single organic material we studied (14 up to date), indicating that a large degree of dynamic disorder is a universal phenomenon in organic crystals. In this paper a new molecular design strategy will be presented to avoid dynamic disorder. We found that small molecules that have their side chains attached to the long axis of their conjugated core have been found to be less likely to suffer from dynamic disorder effects. In particular, we demonstrate that 2,7-dioctyl[1]benzothieno[3,2-b][1]benzothio-phene (C8-BTBT) and 2,9-di-decyl-dinaphtho-[2,3-b:20,30-f]-thieno-[3,2-b]-thiophene (C10DNTT) exhibit strongly reduced thermal vibrations in comparison to other molecules and relate their outstanding performance to their lower dynamic disorder. We rationalize the low degree of dynamic disorder in C8-BTBT and C10-DNTT with a better encapsulation of the conjugated cores in the crystal structure which helps reduce large amplitude thermal motions. The work presented in this paper provides a general strategy for the design of new classes of very high mobility organic semiconductors with low dynamic disorder.

Keywords: charge transport, C8-BTBT, C10-DNTT, dynamic disorder, organic semiconductors, thermal vibrations

Procedia PDF Downloads 395

Authors: A. Alharbi, R. Boukhanouf, T. Habeebullah, H. Ibrahim

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This paper presents a detailed description of evaporative cooling systems used for space cooling in Mina Valley, Saudi Arabia. The thermal performance and environmental impact of the evaporative coolers were evaluated. It was found that the evaporative cooling systems used for space cooling in pilgrims’ accommodations and in the train stations could reduce energy consumption by as much as 75% and cut carbon dioxide emission by 78% compared to traditional vapour compression systems.

Keywords: evaporative cooling, vapor compression, electricity consumption, CO2 emission

Procedia PDF Downloads 424

Authors: JaeHwan Yang, Da-Woon Jeong, Seung-Young Kho, Dong-Kyu Kim

Abstract:

In terms of ITS, information on link characteristic is an essential factor for plan or operation. But in practical cases, not every link has installed sensors on it. The link that does not have data on it is called “Missing Link”. The purpose of this study is to impute data of these missing links. To get these data, this study applies the machine learning method. With the machine learning process, especially for the deep learning process, missing link data can be estimated from present link data. For deep learning process, this study uses “Recurrent Neural Network” to take time-series data of road. As input data, Dedicated Short-range Communications (DSRC) data of Dalgubul-daero of Daegu Metropolitan Area had been fed into the learning process. Neural Network structure has 17 links with present data as input, 2 hidden layers, for 1 missing link data. As a result, forecasted data of target link show about 94% of accuracy compared with actual data.

Keywords: data estimation, link data, machine learning, road network

Procedia PDF Downloads 507

Authors: Johannes Bibinger, Sebastian Eibl, Hans-Joachim Gudladt

Abstract:

This study considers the influence of different irradiation scenarios on the thermal degradation of carbon fiber-reinforced polymers (CFRP). Real threats are simulated, such as fires with long-lasting low heat fluxes and nuclear heat flashes with short-lasting high heat fluxes. For this purpose, coated and uncoated quasi-isotropic samples of the commercially available CFRP HexPly® 8552/IM7 are thermally irradiated from one side by a cone calorimeter and a xenon short-arc lamp with heat fluxes between 5 and 175 W/cm² at varying time intervals. The specimen temperature is recorded on the front and backside as well as at different laminate depths. The CFRP is non-destructively tested with ultrasonic testing, infrared spectroscopy (ATR-FTIR), scanning electron microscopy (SEM), and micro-focused computed X-Ray tomography (μCT). Destructive tests are performed to evaluate the mechanical properties in terms of interlaminar shear strength (ILSS), compressive and tensile strength. The irradiation scenarios vary significantly in heat flux and exposure time. Thus, different heating rates, radiation effects, and temperature distributions occur. This leads to unequal decomposition processes, which affect the sensitivity of the strength type and damage behaviour of the specimens. However, with the use of surface coatings, thermal degradation of composite materials can be delayed.

Keywords: CFRP, one-sided thermal damage, high heat flux, heating rate, non-destructive and destructive testing

Procedia PDF Downloads 104

Authors: Kaoutar Ben Azzou, Hanaa Talei

Abstract:

Human resources is an important department in each organization as it manages the life cycle of employees from recruitment training to retirement or termination of contracts. The recruitment process starts with a job opening, followed by a selection of the best-fit candidates from all applicants. Matching the best profile for a job position requires a manual way of looking at many CVs, which requires hours of work that can sometimes lead to choosing not the best profile. The work presented in this paper aims at reducing the workload of HR personnel by automating the preliminary stages of the candidate screening process, thereby fostering a more streamlined recruitment workflow. This tool introduces an automated system designed to help with the recruitment process by scanning candidates' CVs, extracting pertinent features, and employing machine learning algorithms to decide the most fitting job profile for each candidate. Our work employs natural language processing (NLP) techniques to identify and extract key features from unstructured text extracted from a CV, such as education, work experience, and skills. Subsequently, the system utilizes these features to match candidates with job profiles, leveraging the power of classification algorithms.

Keywords: automated recruitment, candidate screening, machine learning, human resources management

Procedia PDF Downloads 48

Authors: Miguel Angel Calvo Salve

Abstract:

Building enclosures and façades not only have an aesthetic component they must also ensure thermal comfort and improve the acoustics and air quality in buildings. The role of facades design, its assemblies, and construction are key in developing a greener future in architecture. This research and study focus on the design of a multi-leaves building envelope, with a self-supporting wall of granite slabs. The study will demonstrate the advantages of its use in compare with the hanging stone veneer in a vented cladding system. Using the Design of the School of Music and Theatre of the Atlantic Area in Spain as a case study where the multi-leaves enclosure system consists in a self-supported outer leaf of large granite slabs of 15cm. of thickness, a vent cavity with thermal isolation, a brick wall, and a series of internal layers. The methodology used were simulations and data collected in building. The advantages of the self-supporting wall of granite slabs in the outer leaf (15cm). compared with a hanging stone veneer in a vented cladding system can summarize the goals as follows: Using the stone in more natural way, by compression. The weight of the stone slabs goes directly to a strip-footing and don't overload the reinforced concrete structure of the building. The weight of the stone slabs provides an external aerial soundproofing, preventing the sound transmission to the structure. The thickness of the stone slabs is enough to provide the external waterproofing of the building envelope. The self-supporting system with minimum anchorages allows having a continuous and external thermal isolation without thermal bridges. The thickness of ashlars masonry provides a thermal inertia that balances the temperatures between day and night in the external thermal insulation layer. The absence of open joints gives the quality of a continuous envelope transmitting the sensations of the stone, the heaviness in the facade, the rhythm of the music and the sequence of the theatre. The main cost of stone due his bigger thickness is more than compensated with the reduction in assembly costs. Don´t need any substructure systems for hanging stone veneers.

Keywords: self-supporting wall, stone cladding systems, hanging veneer cladding systems, sustainability of facade systems

Procedia PDF Downloads 55